• Genomics & Informatics
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• v.7 no.4
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• pp.217-219
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• 2009

The primary clue for locating protein-coding regions is the open reading frame and the determination of ORFs (Open Reading Frames) is the first step toward the gene prediction, especially for prokaryotes. In this respect, we have developed a web-based ORF search tool called ORF Miner. The ORF Miner is a graphical analysis utility which determines all possible open reading frames of a selectable minimum size in an input sequence. This tool identifies all open reading frames using alternative genetic codes as well as the standard one and reports a list of ORFs with corresponding deduced amino acid sequences. The ORF Miner can be employed for sequence annotation and give a crucial clue to determination of actual protein-coding regions.

• BMB Reports
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• v.40 no.6
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• pp.1021-1027
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• 2007

We demonstrated the genetic polymorphism of aldehyde oxidase (AO) in Donryu strain rats: the ultrarapid metabolizer (UM) with nucleotide mutation of (377G, 2604C) coding for amino acid substitution of (110Gly, 852Val), extensive metabolizer (EM) with (377G/A, 2604C/T) coding for (110Gly/Ser, 852Val/Ala), and poor metabolizer (PM) with (377A, 2604T) coding for (110Ser, 852Ala), respectively. The results suggested that 377G > A and/or 2604C > T should be responsible for the genetic polymorphism. In this study, we constructed an E. coli expression system of four types of AO cDNA including Mut-1 with (377G, 2604T) and Mut-2 with (377A, 2604C) as well as naturally existing nucleotide sequences of UM and PM in order to clarify which one is responsible for the polymorphism. Mut-1 and Mut-2 showed almost the same high and low activity as that of the UM and PM groups, respectively. Thus, the expression study of mutant AO cDNA directly revealed that the nucleotide substitution of 377G > A, but not that of 2604C > T, will play a critical role in the genetic polymorphism of AO in Donryu strain rats. The reason amino acid substitution will cause genetic polymorphism in AO activity was discussed.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.174-181
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• 2004

Recently, many studies have been undergoing to reduce working time in field of machine tool. There are two ways of reducing working time to reduce actual working time by heighten spindle speed and to reduce stand-by time by shortening tool exchange time. Auto tool changer belongs to latter case. Fixed address type auto tool changer can store more number of tools in small space than magazine transfer Ope and can shorten tool exchange time. This study focuses on the topology optimization to reduce the weight of the fixed address type ATC. The optimization program using a real number coding genetic algorithm is developed and is applied to the 10-bar truss optimization problem to verify the developed program. And, it is shown that the developed program gives better results than other methods. Finally, The developed program applied to optimize the fixed address type ATC.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.119-126
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• 2002

In this paper, we present a new approach based on an elitist genetic algorithm for the task of aligning the position of a robot gripper using CCD cameras. The vision-based control scheme for the task of aligning the gripper with the desired position is implemented by image information. The relationship between the camera space location and the robot joint coordinates is estimated using a camera-space parameter modal that generalizes known manipulator kinematics to accommodate unknown relative camera position and orientation. To find the joint angles of a robot manipulator for reaching the target position in the image space, we apply an elitist genetic algorithm instead of a nonlinear least square error method. Since GA employs parallel search, it has good performance in solving optimization problems. In order to improve convergence speed, the real coding method and geometry constraint conditions are used. Experiments are carried out to exhibit the effectiveness of vision-based control using an elitist genetic algorithm with a real coding method.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.456-459
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• 1999

In this paper, we propose a new neural network based on the DNA coding method. The initial population of the structure information and the weights for the neural network is generated, and then the descendants are chose with the Elitist selection by the genetic algorithm. The evolutionary technique and the suitable fitness measure are used to find a neural network with the fractal number of layers. which represents a good approximation to the given function.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.187-190
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• 1995

A method is proposed which searches for optimal structures of Neural Networks (NN) using Genetic Algorithm (GA). The purpose of the method lies in not only finding an optimal NN structure but also leading us to the goal of self-organized control system that acquires its structure and its functionality by itself depending on its environment.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2314-2316
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• 2000

In the construction of successful fuzzy models and/or controllers for nonlinear systems, identification of a good fuzzy Neural inference system is an important yet difficult problem, which is traditionally accomplished by trial and error process. In this paper, we propose a systematic identification procedure for complex multi-input single- output nonlinear systems with DNA coding method.DNA coding method is optimization algorithm based on biological DNA as are conventional genetic algothms (GAs). We also propose a new coding method for applying the DNA coding method to the identification of fuzzy Neural models. To acquire optimal TS fuzzy model with higher accuracy and economical size, we use the DNA coding method to optimize the parameters and the number of fuzzy inference system.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.524-526
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• 1999

In the construction of successful fuzzy models and/or controllers for nonlinear systems, the identification of a good fuzzy inference system is an important yet difficult problem, which is traditionally accomplished by a time-consuming trial-and-error process. In this paper, we propose a systematic identification procedure for complex multi-input single-output nonlinear systems with DNA coding method. A DNA coding method is optimization algorithm based on biological DNA as conventional genetic algorithms(GAs) are. The strings in the DNA coding method are variable-length strings, while standard GAs work with a fixed-length coding scheme. the DNA coding method is well suited to learning because it allows a flexible representation of a fuzzy inference system. We also propose a new coding method fur applying the DNA coding method to the identification of fuzzy models. This coding scheme can effectively represent the zero-order Takagi-Sugeno(TS) fuzzy model. To acquire optimal TS fuzzy model with higher accuracy and economical size, we use the DNA coding method to optimize the parameters and the number of fuzzy inference system. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its application to a Duffing-forced oscillation system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.561-566
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• 2002

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.146-151
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• 2001

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.